PHOTO: FEDERICO ROSTAGNO
AQUAFEED ▶▶▶
Robots, AI, sensor networks and drones in aquaculture?
The latest on precision feeding technologies in this sector, and what’s ahead.
BY TREENA HEIN P
recision feeding is making inroads in all livestock species and, while most advances have been made so far in terrestrial farming of chicken, dairy cattle and other livestock, farming of fish, shrimp and
more is catching up fast. But how far has the use of precision feeding, analytics and optimising feeding practices come in aquaculture and what’s ahead? Dr Francesca Antonucci and Dr Corrado Costa at the Council for Agricultural Research and Agricultural Economics Analysis (CREA) in Italy were pioneers of precision aquaculture, intro- ducing the concept to the sector many years ago and actively researching it ever since. Antonucci observes that particularly over the last two years, “this topic is attracting increasing interest within the aquacul- tural framework, and a review paper we published in 2019 has received increasing numbers of citations, making the work gradually more important from an application point of view.” Precision feeding in aquaculture includes computer vision for animal monitoring, environmental monitoring tools such as sensor networks (wireless and long-range) and robotics, but also the analytics that take the sensor data and turn it into decision tools using Internet of Things (IoT) technology and more.
Putting technologies into practice One example of how precision feeding systems are being ex- plored, says Antonucci, is in salmon farming in coastal areas, where environmental regulations have become stricter in re- sponse to public pressure. A new paper published by a team from global engineering firm Ramboll and two partners shows how automated wireless sensor networks connected to different salmon farm data stations improves the sediment quality that results from discharge and excess feed.
6 ▶ ALL ABOUT FEED | Volume 30, No. 5, 2022
In addition, researchers in Bangladesh have just implemented an IoT-based automated integrated rice-fish farming system that controls portable wireless sensor networks to remotely monitor environmental factors such as dissolved oxygen and turbidity. This reduces waste in feed distribution by allowing more efficient water management and more useful monitor- ing of growth rates to achieve higher production levels. Antonucci also notes that, while robotics are now being widely used in many agricultural applications such as har- vesting, seeding and farm animal monitoring, robotics is just starting to be explored in aquaculture, especially in the use of autonomous vehicles and IoT. “One interesting application is found in a 2022 study by Kape- tanović et al. in Croatia,” she says, “in which an autonomous surface vehicle and a remotely-operated underwater vehicle were used to collect data from both below the sea surface and from the air to analyse fish population modelling. The analyses of photo and video footage allowed non-invasive sampling of fish populations at high frequency, in real time, enabling the measurement of population density and the av- erage size of individuals. The measurement data thus ob- tained are processed statistically and a model data interpreta- tion of the growth of fish in the farm over time is created to estimate the biomass in the net pen allowing ad hoc feeding without waste and excesses.”
Productivity gains Although research is ongoing, it is somewhat possible to pre- dict at this point just how much precision feeding technolo- gies can improve aquaculture productivity compared to the current productivity levels now achieved with various species – and also whether there are specific species where tech can make a bigger difference, and what technologies in particular help the most. For her part, Antonucci points to specific non-invasive tech- nologies that can boost productivity in the ‘big fish’ sector. In her work with Costa, they have shown that computer im- age analysis can provide a non-invasive method for remote monitoring the size, shape, conformation and detailed
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